About the Role
We are looking for a Machine Learning Engineer to build, deploy, and maintain production-grade AI systems that power real products and workflows.
This role focuses on reliability, scalability, and performance. You will work on models that run in real environments, handle real data, and must meet high standards for quality, cost, and latency.
If you care about clean engineering, measurable impact, and turning machine learning into dependable software, this role is for you.
Responsibilities
- Develop and deploy machine learning models into production environments.
- Design scalable inference pipelines and data flows.
- Collaborate with software engineers to integrate ML systems into larger platforms.
- Monitor model performance, drift, and data quality over time.
- Optimize models and systems for speed, cost, and reliability.
- Contribute to ML engineering standards, tooling, and best practices.
- Participate in technical decisions and architecture discussions.
Technical Stack
You will work with technologies such as:
Machine Learning and AI
- Python as the primary language
- Scikit-learn, TensorFlow, or PyTorch
- Feature engineering and model evaluation
- Experiment tracking and reproducibility
Backend and Infrastructure
- REST APIs and microservices
- Docker and container-based deployments
- Cloud infrastructure (AWS, GCP, or similar)
- CI/CD pipelines
Data and Monitoring
- SQL and NoSQL databases
- Data pipelines and batch/stream processing
- Model monitoring and logging
- Basic MLOps tooling and workflows
Requirements
- Strong experience in machine learning engineering or applied data science.
- Solid software engineering fundamentals.
- Experience deploying ML models to production.
- Understanding of system design and scalability.
- Ability to work independently and take ownership.
- Clear communication and collaboration skills.
Nice to Have
- Experience with MLOps platforms or tooling.
- Familiarity with streaming systems or real-time inference.
- Exposure to LLMs or generative AI systems.
- Background in startups or high-growth environments.
What We Offer
- Work on real AI systems used by real users.
- High technical ownership and influence.
- A collaborative, engineering-driven culture.
- Competitive compensation based on experience.
- Flexible working hours and remote-friendly setup.
- Continuous learning and professional growth.
Apply
If you are interested in building robust, production-ready AI systems and want your work to make a tangible impact, we would love to hear from you.
Apply and join us in building AI that actually works.